Efficient Estimation in Marginal Partially Linear Models for Longitudinal/Clustered Data Using Splines

abstract

• We consider marginal semiparametric partially linear models for longitudinal/clustered data and propose an estimation procedure based on a spline approximation of the non-parametric part of the model and an extension of the parametric marginal generalized estimating equations (GEE). Our estimates of both parametric part and non-parametric part of the model have properties parallel to those of parametric GEE, that is, the estimates are efficient if the covariance structure is correctly specified and they are still consistent and asymptotically normal even if the covariance structure is misspecified. By showing that our estimate achieves the semiparametric information bound, we actually establish the efficiency of estimating the parametric part of the model in a stronger sense than what is typically considered for GEE. The semiparametric efficiency of our estimate is obtained by assuming only conditional moment restrictions instead of the strict multivariate Gaussian error assumption. 2007 Board of the Foundation of the Scandinavian Journal of Statistics.

authors

• Zhou, Lan

published proceedings

• SCANDINAVIAN JOURNAL OF STATISTICS

author list (cited authors)

• Huang, J. Z., Zhang, L., & Zhou, L.

citation count

• 52

complete list of authors

• Huang, Jianhua Z||Zhang, Liangyue||Zhou, Lan

publication date

• September 2007

publisher

• Wiley  Publisher

published in

• Scandinavian Journal of Statistics  Journal

keywords

• Clustered Data
• Generalized Estimating Equations
• Longitudinal Data
• Marginal Model
• Non-parametric Regression
• Partially Linear Models
• Polynomial Splines
• Semiparametric Efficiency

Digital Object Identifier (DOI)

• 10.1111/j.1467-9469.2006.00550.x

start page

• 451

end page

• 477

volume

• 34

issue

• 3

URL

• http://dx.doi.org/10.1111/j.1467-9469.2006.00550.x